Detection Method for Abnormal Electroencephalographic Signals Coupled with Discrete Wavelet Transform and Autoencoder
To accurately inspect and interpret electroencephalographic ( EEG ) , a signal anomaly detection method is proposed for identifying seizure signals within EEG recordings.First, EEG signals are decomposed into approximation and detail coefficients through the application of wavelet transform, and the number of wavelet coefficients is limited by discarding insignificant coefficients based on a threshold criterion.Second, an autoencoder is utilized to encode the discrete wavelet coefficients.Then, we analyze EEG signals to detect outliers, reconstruct data through compressed feature sets, and detect epilepsy from non-epileptic signals through a classifier.Finally, the performance of the proposed method is evaluated in comparison with established methods utilizing the University of Bonn database.Experimental results indicate that epileptic seizure signals are detected from EEG signals with classification accuracy and specificity reaching 99.93％ and 100％ respectively, through the employment of linear and nonlinear machine learning classifiers.The robustness and good detection capability of the method in distinguishing epileptic seizure activity within EEG signals are thus demonstrated.This approach is deemed suitable for analyzing time series signals, enabling the simultaneous detection and identification of anomalies.Therefore, it offers an objective reference for the diagnosis, treatment, and evaluation of epilepsy, potentially reducing the workload of medical professionals and enhancing the efficiency of treatment.
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